Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records

Background Parkinson’s disease (PD) is associated with multiple clinical manifestations including motor and non-motor symptoms, and understanding of its etiologies has been informed by a growing number of genetic mutations, and various fluid-based and brain imaging biomarkers. However, the precise mechanisms by which these phenotypic features interact remain elusive. Therefore, we aimed to generate the phenotypic association graph of multiple heterogeneous features within PD to reveal pathological pathways of the complex disease. Methods A data-driven approach was introduced to generate the phenotypic association graphs using data from the Parkinson’s Progression Markers Initiative (PPMI) and Fox Investigation for New Discovery of Biomarkers (BioFIND) studies. We grouped features based on the structure of the learned graphs in both cohorts, and investigated their dynamic patterns in the longitudinal PPMI cohort. Findings 424 patients with PD from the PPMI study and 126 patients with PD from the BioFIND study were available for analysis. For PPMI, the phenotypic association graphs were generated at different time points of the disease, including baseline (without any PD treatments), and 1-, 2-, 3-, 4-, and 5-year follow-up time points. Based on topological structure of the learned graph, clinical features were classified into homogeneous groups, that were densely intra-connected while sparsely inter-connected. Importantly, we observed both stable and longitudinally changing relations in the graphs generated, likely reflecting the dynamic pathologies of PD. By cross-cohort comparison, we observed very similar structure for graphs constructed from BioFIND (in which patients have a much longer duration of PD at enrollment than PPMI) and later-period (4- and 5-year follow-up) data from PPMI. This consistency demonstrates the effectiveness of our method. Interpretation We analyzed the heterogeneous features of PD by generating the phenotypic association graphs. By analyzing the structural relationships among the features over time, our findings could improve the understanding of the pathologies of PD. Funding Michael J Fox Foundation for Parkinson’s Research. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The research is supported by Michael J. Fox Foundation grant number 14858, 14858.01 and 15914. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (). For up-to-date information on the study, visit . PPMI-a public-private partnership-is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid, Biogen, Bristol-Mayers Squibb, Covance, GE, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merk, Meso Scale Discovery, Pfizer, Piramal, Roche, Sanofi, Servier, TEVA, UCB and Golub Capital. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data that support the findings of this study are openly available in PPMI () and BioFIND ()